Machine learning based on clinico-biological features integrated 18F-FDG PET/CT radiomics for distinguishing squamous cell carcinoma from adenocarcinoma of lung

Abstract

Purpose To develop and validate a clinico-biological features and 18F-fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) radiomic-based nomogram via machine learning for the pretherapy prediction of discriminating between adenocarcinoma (ADC) and squamous cell carcinoma (SCC) in non-small cell lung cancer (NSCLC). Methods A total of 315 NSCLC patients confirmed by postoperative pathology between January 2017 and June 2019 were retrospectively analyzed and randomly divided into the training (n = 220) and validation (n = 95) sets. Preoperative clinical factors, serum tumor markers, and PET, and CT radiomic features were analyzed. Prediction models were developed using the least absolute shrinkage and selection operator (LASSO) regression analysis. The performance of the models was evaluated and compared by the area under receiver-operator characteristic (ROC) curve (AUC) and DeLong test. The clinical utility of the models was determined via decision curve analysis (DCA). Then, a nomogram was developed based on the model with the best predictive efficiency and clinical utility and was validated using the calibration plots. Results In total, 122 SCC and 193 ADC patients were enrolled in this study. Four independent prediction models were separately developed to differentiate SCC from ADC using clinical factors-tumor markers, PET radiomics, CT radiomics, and their combination. The DeLong test and DCA showed that the Combined Model, consisting of 2 clinical factors, 2 tumor markers, 7 PET radiomics, and 3 CT radiomic parameters, held the highest predictive efficiency and clinical utility in predicting the NSCLC subtypes compared with the use of these parameters alone in both the training and validation sets (AUCs (95% CIs) = 0.932 (0.900–0.964), 0.901 (0.840–0.957), respectively) (p < 0.05). A quantitative nomogram was subsequently constructed using the independently risk factors from the Combined Model. The calibration curves indicated a good consistency between the actual observations and nomogram predictions. Conclusion This study presents an integrated clinico-biologico-radiological nomogram that can be accurately and noninvasively used for the individualized differentiation SCC from ADC in NSCLC, thereby assisting in clinical decision making for precision treatment.

Evaluation and optimization of radiomics features stability to respiratory motion in 18F‐FDG 3D PET imaging

“…Radiomics is an emerging technology by extracting high‐throughput features based on medical images to characterizing intratumoral heterogeneity, that allows prediction of disease prognosis and aiding clinical decision in several cancers 4–7 . Recently, increasing investigations revealed the great potential of radiomics in lung cancer, including but not limited to differentiate primary or metastatic lesion, epidermal growth factor receptor (EGFR) mutation status, histological subtypes, and predict treatment response 8–12 . However, low stability and reproducibility for most of radiomics features were the major barrier in the generalization of predictive models and clinical translation.…”

“…[4][5][6][7] Recently, increasing investigations revealed the great potential of radiomics in lung cancer, including but not limited to differentiate primary or metastatic lesion, epidermal growth factor receptor (EGFR) mutation status, histological subtypes, and predict treatment response. [8][9][10][11][12] However, low stability and reproducibility for most of radiomics features were the major barrier in the generalization of predictive models and clinical translation. Therefore, identifying stable features are crucial in the field of radiomics.…”

Intratumoral and Peritumoral CT-Based Radiomics Strategy Reveals Distinct Subtypes of Nonsmall-Cell Lung Cancer

“…In recent years, CT-/PET-CT/multimodal MRI-based radiomics strategies have been repeatedly demonstrated to have great capability for the prediction of LUSC and LUAD [2,9,16,[18][19][20]. The diagnostic performance ranged between 0.72 and 0.843.…”

“…In 2016, Wu Chaunzwa et al introduced the convolutional neural network (CNN) to the prediction task and developed a prediction model based on the Visual Geometry Group-16 (VGG-16) network [17], obtaining an optimal AUC of 0.751. In addition, some recent studies also integrated the radiomics strategy with positron emission tomography computed tomography (PET-CT) images, achieving favorable diagnostic performance in the differentiation of these two subtypes of NSCLC [18][19][20]. For instance, Koyasu et al proposed a PET-CTbased radiomics strategy with an extreme gradient boosting (XGBoost) classi er for the prediction task [19], achieving good performance with an AUC of 0.843.…”

Development of a PET/CT Molecular Radiomics-clinical Model to Predict Local Lymph Node Metastasis of Invasive Lung Adenocarcinoma (≤ 3cm)

“…The two doctors delineated the primary tumor on PET images, using a 40% SUVmax threshold to characterize the volume of interest (VOI) [17][18][19][20][21]. To avoid including the physiologic uptake in the VOI, a combined CT and PET scan reading is performed [19][20][21]. An example of VOI delineation is shown in Fig.…”